Responsible Federated Learning in Smart Transportation: Outlooks and Challenges

Journal Publication ResearchOnline@JCU
Huang, Xiaowen;Huang, Tao;Gu, Shushi;Zhao, Shuguang;Zhang, Guanglin
Abstract

Integrating artificial intelligence (AI) and federated learning (FL) in smart transportation has raised critical issues regarding their responsible use. Ensuring responsible AI is paramount for the stability and sustainability of intelligent transportation systems. Despite its importance, research on the responsible application of AI and FL in this domain remains nascent, with a paucity of in-depth investigations into their confluence. Our study analyzes the roles of FL in smart transportation, as well as the promoting effect of responsible AI on distributed smart transportation. Lastly, we discuss the challenges of developing and implementing responsible FL in smart transportation and propose potential solutions. By integrating responsible AI and FL, intelligent transportation systems are expected to achieve a higher degree of intelligence, personalization, safety, and transparency.

Journal

IEEE Internet of Things Magazine

Publication Name

IEEE Internet of Things Magazine

Volume

7

ISBN/ISSN

2576-3199

Edition

N/A

Issue

5

Pages Count

7

Location

N/A

Publisher

Institute of Electrical and Electronics Engineers

Publisher Url

N/A

Publisher Location

N/A

Publish Date

N/A

Url

N/A

Date

N/A

EISSN

N/A

DOI

10.1109/IOTM.001.2300286